{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"mc_dropout_homoscedastic.ipynb","version":"0.3.2","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"xAYPKSFrG8AF","colab_type":"code","outputId":"f65fc439-a155-4cb7-8686-423c8f13731f","executionInfo":{"status":"ok","timestamp":1557339111497,"user_tz":-60,"elapsed":21052,"user":{"displayName":"Stratis Markou","photoUrl":"","userId":"09754366312766083286"}},"colab":{"base_uri":"https://localhost:8080/","height":258}},"source":["!pip3 install http://download.pytorch.org/whl/cu92/torch-0.4.1-cp36-cp36m-linux_x86_64.whl\n","!pip3 install torchvision\n","! pip install GPy\n","import GPy\n","import time\n","import copy\n","import math\n","import matplotlib.pyplot as plt\n","import numpy as np\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","from torch.autograd import Variable\n","from torch.optim import Optimizer\n","from torch.optim.sgd import SGD\n","\n","from torchvision import datasets, transforms\n","from torchvision.utils import make_grid\n","from tqdm import tqdm, trange\n","from google.colab import files\n","%config InlineBackend.figure_format = 'svg'"],"execution_count":7,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: torch==0.4.1 from http://download.pytorch.org/whl/cu92/torch-0.4.1-cp36-cp36m-linux_x86_64.whl in /usr/local/lib/python3.6/dist-packages (0.4.1)\n","Requirement already satisfied: torchvision in /usr/local/lib/python3.6/dist-packages (0.2.2.post3)\n","Requirement already satisfied: torch in /usr/local/lib/python3.6/dist-packages (from torchvision) (0.4.1)\n","Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.6/dist-packages (from torchvision) (4.3.0)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torchvision) (1.16.3)\n","Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from torchvision) (1.12.0)\n","Requirement already satisfied: olefile in /usr/local/lib/python3.6/dist-packages (from pillow>=4.1.1->torchvision) (0.46)\n","Requirement already satisfied: GPy in /usr/local/lib/python3.6/dist-packages (1.9.6)\n","Requirement already satisfied: numpy>=1.7 in /usr/local/lib/python3.6/dist-packages (from GPy) (1.16.3)\n","Requirement already satisfied: scipy>=0.16 in /usr/local/lib/python3.6/dist-packages (from GPy) (1.2.1)\n","Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from GPy) (1.12.0)\n","Requirement already satisfied: paramz>=0.9.0 in /usr/local/lib/python3.6/dist-packages (from GPy) (0.9.4)\n","Requirement already satisfied: decorator>=4.0.10 in /usr/local/lib/python3.6/dist-packages (from paramz>=0.9.0->GPy) (4.4.0)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"1-7dNcVmHA3I","colab_type":"code","outputId":"0493c51d-2c3d-4a99-ed05-f2e1c02751f1","executionInfo":{"status":"ok","timestamp":1557339111498,"user_tz":-60,"elapsed":18053,"user":{"displayName":"Stratis Markou","photoUrl":"","userId":"09754366312766083286"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["torch.cuda.device(0)\n","torch.cuda.get_device_name(torch.cuda.current_device())"],"execution_count":8,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'Tesla K80'"]},"metadata":{"tags":[]},"execution_count":8}]},{"cell_type":"code","metadata":{"id":"_1S5kt0omQ-N","colab_type":"code","colab":{}},"source":["def to_variable(var=(), cuda=True, volatile=False):\n","    out = []\n","    for v in var:\n","        \n","        if isinstance(v, np.ndarray):\n","            v = torch.from_numpy(v).type(torch.FloatTensor)\n","\n","        if not v.is_cuda and cuda:\n","            v = v.cuda()\n","\n","        if not isinstance(v, Variable):\n","            v = Variable(v, volatile=volatile)\n","\n","        out.append(v)\n","    return out"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Va8V78eFFsc9","colab_type":"code","colab":{}},"source":["def log_gaussian_loss(output, target, sigma, no_dim):\n","    exponent = -0.5*(target - output)**2/sigma**2\n","    log_coeff = -no_dim*torch.log(sigma)\n","    \n","    return -(log_coeff + exponent).sum()\n","\n","\n","def get_kl_divergence(weights, prior, varpost):\n","    prior_loglik = prior.loglik(weights)\n","    \n","    varpost_loglik = varpost.loglik(weights)\n","    varpost_lik = varpost_loglik.exp()\n","    \n","    return (varpost_lik*(varpost_loglik - prior_loglik)).sum()\n","\n","\n","class gaussian:\n","    def __init__(self, mu, sigma):\n","        self.mu = mu\n","        self.sigma = sigma\n","        \n","    def loglik(self, weights):\n","        exponent = -0.5*(weights - self.mu)**2/self.sigma**2\n","        log_coeff = -0.5*(np.log(2*np.pi) + 2*np.log(self.sigma))\n","        \n","        return (exponent + log_coeff).sum()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"colab_type":"code","id":"ASGi2Ecx5G-F","colab":{}},"source":["class MC_Dropout_Layer(nn.Module):\n","    def __init__(self, input_dim, output_dim, dropout_prob):\n","        super(MC_Dropout_Layer, self).__init__()\n","        self.input_dim = input_dim\n","        self.output_dim = output_dim\n","        self.dropout_prob = dropout_prob\n","        \n","        self.weights = nn.Parameter(torch.Tensor(self.input_dim, self.output_dim).uniform_(-0.01, 0.01))\n","        self.biases = nn.Parameter(torch.Tensor(self.output_dim).uniform_(-0.01, 0.01))\n","        \n","    def forward(self, x):\n","        \n","        dropout_mask = torch.bernoulli((1 - self.dropout_prob)*torch.ones(self.weights.shape)).cuda()\n","        \n","        return torch.mm(x, self.weights*dropout_mask) + self.biases"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"colab_type":"code","id":"_8dV-QIq5G-I","colab":{}},"source":["class MC_Dropout_Model(nn.Module):\n","    def __init__(self, input_dim, output_dim, no_units, init_log_noise):\n","        super(MC_Dropout_Model, self).__init__()\n","        \n","        self.input_dim = input_dim\n","        self.output_dim = output_dim\n","        \n","        self.layer1 = nn.Linear(input_dim, no_units)\n","        self.layer2 = nn.Linear(no_units, output_dim)\n","        \n","        # activation to be used between hidden layers\n","        self.activation = nn.ReLU(inplace = True)\n","        self.log_noise = nn.Parameter(torch.cuda.FloatTensor([init_log_noise]))\n","\n","    \n","    def forward(self, x):\n","        \n","        x = x.view(-1, self.input_dim)\n","        \n","        x = self.layer1(x)\n","        x = self.activation(x)\n","        \n","        x = F.dropout(x, p=0.5, training=True)\n","        \n","        x = self.layer2(x)\n","        \n","        return x"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"colab_type":"code","id":"oAYelw3B5G-K","colab":{}},"source":["class MC_Dropout_Wrapper:\n","    def __init__(self, input_dim, output_dim, no_units, learn_rate, batch_size, no_batches, weight_decay, init_log_noise):\n","        \n","        self.learn_rate = learn_rate\n","        self.batch_size = batch_size\n","        self.no_batches = no_batches\n","        \n","        self.network = MC_Dropout_Model(input_dim = input_dim, output_dim = output_dim,\n","                                        no_units = no_units, init_log_noise = init_log_noise)\n","        self.network.cuda()\n","        \n","        self.optimizer = torch.optim.SGD(self.network.parameters(), lr=learn_rate, weight_decay=weight_decay)\n","        self.loss_func = log_gaussian_loss\n","    \n","    def fit(self, x, y):\n","        x, y = to_variable(var=(x, y), cuda=True)\n","        \n","        # reset gradient and total loss\n","        self.optimizer.zero_grad()\n","        \n","        output = self.network(x)\n","        loss = self.loss_func(output, y, torch.exp(self.network.log_noise), 1)/len(x)\n","        \n","        loss.backward()\n","        self.optimizer.step()\n","\n","        return loss"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"ym6HBK-s8GnO","colab_type":"code","outputId":"204c815b-505a-4785-9488-76ee13a77837","executionInfo":{"status":"ok","timestamp":1557340399700,"user_tz":-60,"elapsed":4257,"user":{"displayName":"Stratis Markou","photoUrl":"","userId":"09754366312766083286"}},"colab":{"base_uri":"https://localhost:8080/","height":187}},"source":["np.random.seed(2)\n","no_points = 400\n","lengthscale = 1\n","variance = 1.0\n","sig_noise = 0.3\n","x = np.random.uniform(-3, 3, no_points)[:, None]\n","x.sort(axis = 0)\n","\n","\n","k = GPy.kern.RBF(input_dim = 1, variance = variance, lengthscale = lengthscale)\n","C = k.K(x, x) + np.eye(no_points)*sig_noise**2\n","\n","y = np.random.multivariate_normal(np.zeros((no_points)), C)[:, None]\n","y = (y - y.mean())\n","x_train = x[75:325]\n","y_train = y[75:325]\n","\n","num_epochs, batch_size, nb_train = 2000, len(x_train), len(x_train)\n","\n","net = MC_Dropout_Wrapper(input_dim = 1, output_dim=1, no_units=200, learn_rate=1e-2,\n","                         batch_size=batch_size, no_batches=1, init_log_noise=0, weight_decay=1e-2)\n","\n","for i in range(num_epochs):\n","    \n","    loss = net.fit(x_train, y_train)\n","    \n","    if i % 200 == 0:\n","        print('Epoch: %4d, Train loss = %7.3f, noise = %6.3f' % \\\n","              (i, loss.cpu().data.numpy(), torch.exp(net.network.log_noise).cpu().data.numpy()))"],"execution_count":69,"outputs":[{"output_type":"stream","text":["Epoch:    0, Train loss =   1.353, noise =  1.017\n","Epoch:  200, Train loss =   0.178, noise =  0.772\n","Epoch:  400, Train loss =   0.093, noise =  0.669\n","Epoch:  600, Train loss =   0.022, noise =  0.620\n","Epoch:  800, Train loss =  -0.081, noise =  0.595\n","Epoch: 1000, Train loss =  -0.058, noise =  0.573\n","Epoch: 1200, Train loss =  -0.137, noise =  0.566\n","Epoch: 1400, Train loss =  -0.099, noise =  0.551\n","Epoch: 1600, Train loss =  -0.094, noise =  0.546\n","Epoch: 1800, Train loss =  -0.085, noise =  0.543\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"tvP-HbNH_Uen","colab_type":"code","outputId":"2121d981-6bda-4313-88e4-a67aee91578f","executionInfo":{"status":"error","timestamp":1557339949104,"user_tz":-60,"elapsed":7414,"user":{"displayName":"Stratis Markou","photoUrl":"","userId":"09754366312766083286"}},"colab":{"base_uri":"https://localhost:8080/","height":815}},"source":["samples = []\n","noises = []\n","for i in range(1000):\n","    preds = net.network.forward(torch.linspace(-5, 5, 200).cuda()).cpu().data.numpy()\n","    samples.append(preds)\n","    \n","samples = np.array(samples)\n","means = (samples.mean(axis = 0)).reshape(-1)\n","\n","aleatoric = torch.exp(net.network.log_noise).cpu().data.numpy()\n","epistemic = (samples.var(axis = 0)**0.5).reshape(-1)\n","total_unc = (aleatoric**2 + epistemic**2)**0.5\n","\n","\n","c = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd',\n","     '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf']\n","\n","plt.figure(figsize = (6, 5))\n","plt.style.use('default')\n","plt.scatter(x_train, y_train, s = 10, marker = 'x', color = 'black', alpha = 0.5)\n","plt.fill_between(np.linspace(-5, 5, 200), means + aleatoric, means + total_unc, color = c[0], alpha = 0.3, label = 'Epistemic + Aleatoric')\n","plt.fill_between(np.linspace(-5, 5, 200), means - total_unc, means - aleatoric, color = c[0], alpha = 0.3)\n","plt.fill_between(np.linspace(-5, 5, 200), means - aleatoric, means + aleatoric, color = c[1], alpha = 0.4, label = 'Aleatoric')\n","plt.plot(np.linspace(-5, 5, 200), means, color = 'black', linewidth = 1)\n","plt.xlim([-5, 5])\n","plt.ylim([-5, 7])\n","plt.xlabel('$x$', fontsize=30)\n","plt.title('MC dropout', fontsize=40)\n","plt.tick_params(labelsize=30)\n","plt.xticks(np.arange(-4, 5, 2))\n","plt.yticks(np.arange(-4, 7, 2))\n","plt.gca().set_yticklabels([])\n","plt.gca().yaxis.grid(alpha=0.3)\n","plt.gca().xaxis.grid(alpha=0.3)\n","plt.savefig('mc_dropout.pdf', bbox_inches = 'tight')\n","\n","files.download(\"mc_dropout.pdf\")\n","\n","plt.show()"],"execution_count":62,"outputs":[{"output_type":"error","ename":"ValueError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)","\u001b[0;32m<ipython-input-62-6e1f8f2c628f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstyle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'default'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmarker\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'x'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'black'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfill_between\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinspace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m200\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeans\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0maleatoric\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeans\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mtotal_unc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Epistemic + Aleatoric'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfill_between\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinspace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m200\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeans\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mtotal_unc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeans\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0maleatoric\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfill_between\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinspace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m200\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeans\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0maleatoric\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeans\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0maleatoric\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Aleatoric'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mfill_between\u001b[0;34m(x, y1, y2, where, interpolate, step, data, **kwargs)\u001b[0m\n\u001b[1;32m   2606\u001b[0m     return gca().fill_between(\n\u001b[1;32m   2607\u001b[0m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwhere\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterpolate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minterpolate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2608\u001b[0;31m         **({\"data\": data} if data is not None else {}), 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\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1811\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1812\u001b[0m         inner.__doc__ = _add_data_doc(inner.__doc__,\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mfill_between\u001b[0;34m(self, x, y1, y2, where, interpolate, step, **kwargs)\u001b[0m\n\u001b[1;32m   5057\u001b[0m             \u001b[0mwhere\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5058\u001b[0m         where = where & ~functools.reduce(np.logical_or,\n\u001b[0;32m-> 5059\u001b[0;31m                                           map(np.ma.getmask, [x, y1, y2]))\n\u001b[0m\u001b[1;32m   5060\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5061\u001b[0m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m 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